Oni279/BanglaRQA

Tasks

• question-answering

Citation text from the card

@inproceedings{ekram-etal-2022-banglarqa,
    title = "{B}angla{RQA}: A Benchmark Dataset for Under-resourced {B}angla Language Reading Comprehension-based Question Answering with Diverse Question-Answer Types",
    author = "Ekram, Syed Mohammed Sartaj  and
      Rahman, Adham Arik  and
      Altaf, Md. Sajid  and
      Islam, Mohammed Saidul  and
      Rahman, Mehrab Mustafy  and
      Rahman, Md Mezbaur  and
      Hossain, Md Azam  and
      Kamal, Abu Raihan Mostofa",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-emnlp.186",
    pages = "2518--2532",
    abstract = "High-resource languages, such as English, have access to a plethora of datasets with various question-answer types resembling real-world reading comprehension. However, there is a severe lack of diverse and comprehensive question-answering datasets in under-resourced languages like Bangla. The ones available are either translated versions of English datasets with a niche answer format or created by human annotations focusing on a specific domain, question type, or answer type. To address these limitations, this paper introduces BanglaRQA, a reading comprehension-based Bangla question-answering dataset with various question-answer types. BanglaRQA consists of 3,000 context pas

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